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Novel Switched-Capacitor Compact Multilevel Converter Based on Packed E-Cell Design with Fault Tolerant Operation

2021· article· en· W3213581457 on OpenAlexaff
Mohammad Sharifzadeh, Mohammadali Ahmadijokani, Majid Mehrasa, Mahdieh S. Sadabadi, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCapacitorRedundancy (engineering)Switched capacitorTopology (electrical circuits)VoltageElectronic engineeringComputer scienceBoosting (machine learning)MATLABElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper a novel single dc-source switch-capacitor compact multilevel topology is designed based on Packed E-Cell to have the boosting potency for the 11-level operation and to merit as a reliable AC-DC converter for fault tolerant capability. The proposed single-phase compact converter is reduced the active/passive components counts while the output voltage levels are increased which it includes 6 normal power switches and 3 bidirectional switches as well as 2 dc capacitors as the auxiliary dc sources. While the conventional compact converter is incapable of fault tolerant operation, the proposed topology could emerge as a reliable while it has the boosting ability due to its switched-capacitor feature. Moreover, the dc capacitors are actively balanced thanks to various switching states redundancy. The proposed single-phase switched-capacitor converter is validated by theoretical analyses as well as simulation results obtained by Matlab-Simulink where it shows the accurate dc capacitors voltages balancing and prefect 11-level generation in the AC output terminal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.210
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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